From 4538fc52c33df7d4fbcd5dd9b0fbb9fff4e7fbdb Mon Sep 17 00:00:00 2001
From: slobentanzer
This manuscript
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+(permalink)
was automatically generated
-from biocypher/biochatter-paper@e0f5db3
+from biocypher/biochatter-paper@ccfa0d0
on February 15, 2024.
Despite technological advances, understanding biological and biomedical systems still poses major challenges [1,2].
We measure more and more data points with ever-increasing resolution to such a degree that their analysis and interpretation have become the bottleneck for their exploitation [2].
One reason for this challenge may be the inherent limitation of human knowledge [3]: Even seasoned domain experts cannot know the implications of every gene, molecule, symptom, or biomarker.
-In addition, biological events are context-dependent, for instance, with respect to a cell type or specific disease.A Platform for the Biomedical Application of Large Language Mo
Authors
@@ -303,7 +303,7 @@ Introduction
Large Language Models (LLMs) of the current generation, in contrast, can access enormous amounts of knowledge, encoded (incomprehensibly) in their billions of parameters [4,5,6,7].
Trained correctly, they can recall and combine virtually limitless knowledge from their training set.
ChatGPT has taken the world by storm, and many biomedical researchers already use LLMs in their daily work, for general as well as research tasks [8,9,10].
diff --git a/manuscript.pdf b/manuscript.pdf
index ef129dd24aefc70894fb52235bdad34eda5bb0e8..26d6825315e0fffac1ffa76daacb369d01dd48ff 100644
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